More than 80 % of all information in an organization is unstructured, created by knowledge workers engaged in peer-to-peer networks of expertise to share knowledge across organizational boundaries. Enterprise Information Systems (EIS) do not integrate unstructured information. At best, they integrate links to unstructured information connected with structured information in their databases. The amount of unstructured information is rising quickly. Ensuring the quality of this unstructured information is difficult. It is often inaccessible, unavailable, incomplete, irrelevant, untimely, inaccurate, and/or incomprehensible. It becomes problematic to reconstruct what has happened in organizations. When used for organizational policies, decisions, products, actions and transactions, structured and unstructured information are called records. They are an entity of information, consisting out of an information object (structured or unstructured) and its metadata. They are important for organizational accountability and business process performance, for without them reconstruction of past happenings and meaningful production become an impossibility. Organization-wide management of records is not a common functionality for EIS, resulting in [1] a fragmentation in the management of records, where structured and unstructured information objects are stored in a variety of systems, unconnected with their metadata; [2] a fragmentation in metadata management, leading to a loss of contextuality because metadata are separated from their information objects; and [3] a declining quality or records, because their provenance, integrity, and preservation are in peril. Organizational accountability is based on records and their context to reconstruct the past. Because records are not controlled by EIS, they can only marginally be used for accountability. The challenge for organizational accountability is to generate trusted records, fixed and contextual information objects inseparately linked with metadata that capture context to regain evidential value and to allow for the reconstruction of the past. The research question of this paper is how to capture records and their context within EIS to regain the evidential value of records to allow for a more robust organizational accountability. To find an answer, we need to pay attention to the concept of context, on how to capture context in metadata, and how to embed and manage records in EIS.
Twee jaar geleden schreven Cloosterman & van Loo in TvM dat accountability bovenaan de lijst prijkt van kansen voor marketeer. De aandacht voor accountability is sindsdien alleen maar toegenomen. Vanwege de economische recessie ondervinden reclame- en communicatiebureaus meer en meer druk om de effectiviteit van hun campagnes, en daarmee hun bestaansrecht, aan te tonen.
Many organizations have undergone substantial reorganization in the last decade. They re-engineered their business processes and exchanged proprietary, not integrated applications for more standard solutions. Integration of structured data in relational databases has improved documentation of business transactions and increased data quality. But almost 90% of the information that organizations manage is unstructured, cannot easily be integrated into a traditional database. When used for organizational actions and transactions, structured and unstructured information are records. They are meant and used as evidence. Governments, courts and other stakeholders are making increasing demands for the trustworthiness of records. An analysis of literature of the information, organization and archival sciences illustrates that accountability needs the reconstruction of the past. Hypothesis of this paper is that for the reconstruction of the past each organization needs a combination of threemechanisms: enterprise records management, organizational memory and records auditing. Enterprise records management ensures that records meet the quality requirements needed for accountability: integrity, authenticity, controllability and historicity. They ensure records that can be trusted and enhance the possibilities for the reconstruction of the past. The organizational memory ensures that trusted records are preserved for as long as is necessary to comply with accountability regulations. It provides an ICT infrastructure to (indefinitely) store those records and to keep them accessible. Records auditing researches the first two mentioned mechanisms to assess the possibility to reconstruct past organizational actions and transactions. These mechanisms ensure that organizations have a documented understanding of [1] the processing of actions and transactions within business processes; [2] the dissemination of trusted records; [3] the way the organization accounts for the actions and transactions within its business processes; and [4] the reconstruction of actions and transactions from business processes over time. This understanding is crucial for the reconstruction of the past and for organizational accountability.
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Moderatie van lezersreacties onder nieuwsartikelen is erg arbeidsintensief. Met behulp van kunstmatige intelligentie wordt moderatie mogelijk tegen een redelijke prijs. Aangezien elke toepassing van kunstmatige intelligentie eerlijk en transparant moet zijn, is het belangrijk om te onderzoeken hoe media hieraan kunnen voldoen.
Moderatie van lezersreacties onder nieuwsartikelen is erg arbeidsintensief. Met behulp van kunstmatige intelligentie wordt moderatie mogelijk tegen een redelijke prijs. Aangezien elke toepassing van kunstmatige intelligentie eerlijk en transparant moet zijn, is het belangrijk om te onderzoeken hoe media hieraan kunnen voldoen.Doel Dit promotieproject zal zich richten op de rechtvaardigheid, accountability en transparantie van algoritmische systemen voor het modereren van lezersreacties. Het biedt een theoretisch kader en bruikbare matregelen die nieuwsorganisaties zullen ondersteunen in het naleven van recente beleidsvorming voor een waardegedreven implementatie van AI. Nu steeds meer nieuwsmedia AI gaan gebruiken, moeten ze rechtvaardigheid, accountability en transparantie in hun gebruik van algoritmen meenemen in hun werkwijzen. Resultaten Hoewel moderatie met AI zeer aantrekkelijk is vanuit economisch oogpunt, moeten nieuwsmedia weten hoe ze onnauwkeurigheid en bias kunnen verminderen (fairness), de werking van hun AI bekendmaken (accountability) en de gebruikers laten begrijpen hoe beslissingen via AI worden genomen (transparancy). Dit proefschrift bevordert de kennis over deze onderwerpen. Looptijd 01 februari 2022 - 01 februari 2025 Aanpak De centrale onderzoeksvraag van dit promotieonderzoek is: Hoe kunnen en moeten nieuwsmedia rechtvaardigheid, accountability en transparantie in hun gebruik van algoritmes voor commentmoderatie? Om deze vraag te beantwoorden is het onderzoek opgesplitst in vier deelvragen. Hoe gebruiken nieuwsmedia algoritmes voor het modereren van reacties? Wat kunnen nieuwsmedia doen om onnauwkeurigheid en bias bij het modereren via AI van reacties te verminderen? Wat moeten nieuwsmedia bekendmaken over hun gebruik van moderatie via AI? Wat maakt uitleg van moderatie via AI begrijpelijk voor gebruikers van verschillende niveaus van digitale competentie?
Smart city technologies, including artificial intelligence and computer vision, promise to bring a higher quality of life and more efficiently managed cities. However, developers, designers, and professionals working in urban management have started to realize that implementing these technologies poses numerous ethical challenges. Policy papers now call for human and public values in tech development, ethics guidelines for trustworthy A.I., and cities for digital rights. In a democratic society, these technologies should be understandable for citizens (transparency) and open for scrutiny and critique (accountability). When implementing such public values in smart city technologies, professionals face numerous knowledge gaps. Public administrators find it difficult to translate abstract values like transparency into concrete specifications to design new services. In the private sector, developers and designers still lack a ‘design vocabulary’ and exemplary projects that can inspire them to respond to transparency and accountability demands. Finally, both the public and private sectors see a need to include the public in the development of smart city technologies but haven’t found the right methods. This proposal aims to help these professionals to develop an integrated, value-based and multi-stakeholder design approach for the ethical implementation of smart city technologies. It does so by setting up a research-through-design trajectory to develop a prototype for an ethical ‘scan car’, as a concrete and urgent example for the deployment of computer vision and algorithmic governance in public space. Three (practical) knowledge gaps will be addressed. With civil servants at municipalities, we will create methods enabling them to translate public values such as transparency into concrete specifications and evaluation criteria. With designers, we will explore methods and patterns to answer these value-based requirements. Finally, we will further develop methods to engage civil society in this processes.
